UNLABELLED

is to train and test an ensemble of machine learning models, as well as to compare its performance with the BERT language model pre-trained on medical data to perform simple binary classification, i.e., determine the presence/absence of the signs of intracranial hemorrhage (ICH) in brain CT reports.

MATERIALS AND METHODS

Seven machine learning algorithms and three text vectorization techniques were selected as models to solve the binary classification problem. These models were trained on textual data represented by 3980 brain CT reports from 56 inpatient medical facilities in Moscow. The study utilized three text vectorization techniques: bag of words, TF-IDF, and word2vec. The resulting data were then processed by the following machine learning algorithms: decision tree, random forest, logistic regression, nearest neighbors, support vector machines, Catboost, and XGboost. Data analysis and pre-processing were performed using NLTK (Natural Language Toolkit, version 3.6.5), libraries for character-based and statistical processing of natural language, and Scikit-learn (version 0.24.2), a library for machine learning containing tools to tackle classification challenges. MedRuBertTiny2 was taken as a BERT transformer model pre-trained on medical data.

RESULTS

Based on the training and testing outcomes from seven machine learning algorithms, the authors selected three algorithms that yielded the highest metrics (i.e. sensitivity and specificity): CatBoost, logistic regression, and nearest neighbors. The highest metrics were achieved by the bag of words technique. These algorithms were assembled into an ensemble using the stacking technique. The sensitivity and specificity for the validation dataset separated from the original sample were 0.93 and 0.90, respectively. Next, the ensemble and the BERT model were trained on an independent dataset containing 9393 textual radiology reports also divided into training and test sets. Once the ensemble was tested on this dataset, the resulting sensitivity and specificity were 0.92 and 0.90, respectively. The BERT model tested on these data demonstrated a sensitivity of 0.97 and a specificity of 0.90.

CONCLUSION

When analyzing textual reports of brain CT scans with signs of intracranial hemorrhage, the trained ensemble demonstrated high accuracy metrics. Still, manual quality control of the results is required during its application. The pre-trained BERT transformer model, additionally trained on diagnostic textual reports, demonstrated higher accuracy metrics (p<0.05). The results show promise in terms of finding specific values for both binary classification task and in-depth analysis of unstructured medical information.